Context-driven enforcement gateway for data agents, powered by DataHub. DataHub gives agents context; Gatehouse makes context decide what agents may do.
Gatehouse defines 3 tools with clear names and reasonable descriptions. All tools have input schemas with typed parameters and descriptions. However, output schemas are not documented, error handling lacks recovery guidance, and parameter descriptions could be more detailed about constraints and formats. The tools follow verb_noun naming (run_sql, report_data_issue, gatehouse_whoami) and address distinct concerns, but descriptions are brief (50-100 chars) and lack context about when to use each tool or what happens on failure.
Control tool that returns the identity and permissions of the calling agent
Report a data quality issue against a dataset, which is recorded in DataHub
Execute SQL queries against the warehouse with policy enforcement and governance
Output schemas not documented. LLMs cannot plan downstream calls or extract required fields without knowing what each tool returns.
Parameter descriptions lack format/constraint details. 'sql' parameter has no guidance on dialect, size limits, or allowed operations. 'severity' enum values not documented.
Error handling not visible in tool definitions. No recovery guidance for SQL syntax errors, permission denials, or data quality issue submission failures.
Tool descriptions are brief (50-100 chars) and lack context about prerequisites, side effects, or when to use each tool vs alternatives.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | C | 60 | <=2025-11-25 | v2 |
run_sql accepts free-form SQL with no visible input validation or injection protection documented. LLMs could pass malicious queries.